
"""simple docstring"""






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):
			lowercase        =     ['''pixel_values''']






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Dict[str, int] = None      ,SCREAMING_SNAKE_CASE_  :	PILImageResampling = PILImageResampling.BILINEAR      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Union[int, float] = 1 / 255      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Dict[str, int] = None      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,**SCREAMING_SNAKE_CASE_  :	Any      ,)       ->	None:





				"""simple docstring"""


				super().__init__(**SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  size if size is not None else {'''shortest_edge''': 224}
				lowerCAmelCase												=  get_size_dict(SCREAMING_SNAKE_CASE_      ,default_to_square=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
				lowerCAmelCase												=  get_size_dict(SCREAMING_SNAKE_CASE_      ,param_name='''crop_size'''			)

				lowerCAmelCase												=  do_resize
				lowerCAmelCase												=  size
				lowerCAmelCase												=  resample
				lowerCAmelCase												=  do_rescale
				lowerCAmelCase												=  rescale_factor
				lowerCAmelCase												=  do_center_crop
				lowerCAmelCase												=  crop_size
				lowerCAmelCase												=  do_flip_channel_order






			def 					UpperCAmelCase							(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	np.ndarray      ,SCREAMING_SNAKE_CASE_  :	Dict[str, int]      ,SCREAMING_SNAKE_CASE_  :	PILImageResampling = PIL.Image.BILINEAR      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, ChannelDimension]] = None      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]      ,)       ->	np.ndarray:





				"""simple docstring"""


				lowerCAmelCase												=  get_size_dict(SCREAMING_SNAKE_CASE_      ,default_to_square=SCREAMING_SNAKE_CASE_			)
				if "shortest_edge" not in size:
					raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""			)
				lowerCAmelCase												=  get_resize_output_image_size(SCREAMING_SNAKE_CASE_      ,size=size['''shortest_edge''']      ,default_to_square=SCREAMING_SNAKE_CASE_			)
				return resize(SCREAMING_SNAKE_CASE_      ,size=SCREAMING_SNAKE_CASE_      ,resample=SCREAMING_SNAKE_CASE_      ,data_format=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str      ,SCREAMING_SNAKE_CASE_  :	np.ndarray      ,SCREAMING_SNAKE_CASE_  :	Dict[str, int]      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, ChannelDimension]] = None      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,)       ->	np.ndarray:





				"""simple docstring"""


				lowerCAmelCase												=  get_size_dict(SCREAMING_SNAKE_CASE_			)
				if "height" not in size or "width" not in size:
					raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""			)
				return center_crop(SCREAMING_SNAKE_CASE_      ,size=(size['''height'''], size['''width'''])      ,data_format=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	np.ndarray      ,SCREAMING_SNAKE_CASE_  :	Union[int, float]      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, ChannelDimension]] = None      ,**SCREAMING_SNAKE_CASE_  :	int      ,)       ->	Optional[int]:





				"""simple docstring"""


				return rescale(SCREAMING_SNAKE_CASE_      ,scale=SCREAMING_SNAKE_CASE_      ,data_format=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	np.ndarray      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, ChannelDimension]] = None			)       ->	np.ndarray:





				"""simple docstring"""


				return flip_channel_order(SCREAMING_SNAKE_CASE_      ,data_format=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	ImageInput      ,SCREAMING_SNAKE_CASE_  :	bool = None      ,SCREAMING_SNAKE_CASE_  :	Dict[str, int] = None      ,SCREAMING_SNAKE_CASE_  :	PILImageResampling = None      ,SCREAMING_SNAKE_CASE_  :	bool = None      ,SCREAMING_SNAKE_CASE_  :	float = None      ,SCREAMING_SNAKE_CASE_  :	bool = None      ,SCREAMING_SNAKE_CASE_  :	Dict[str, int] = None      ,SCREAMING_SNAKE_CASE_  :	bool = None      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, TensorType]] = None      ,SCREAMING_SNAKE_CASE_  :	ChannelDimension = ChannelDimension.FIRST      ,**SCREAMING_SNAKE_CASE_  :	Dict      ,)       ->	PIL.Image.Image:





				"""simple docstring"""


				lowerCAmelCase												=  do_resize if do_resize is not None else self.do_resize
				lowerCAmelCase												=  resample if resample is not None else self.resample
				lowerCAmelCase												=  do_rescale if do_rescale is not None else self.do_rescale
				lowerCAmelCase												=  rescale_factor if rescale_factor is not None else self.rescale_factor
				lowerCAmelCase												=  do_center_crop if do_center_crop is not None else self.do_center_crop
				lowerCAmelCase												=  (
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				lowerCAmelCase												=  size if size is not None else self.size
				lowerCAmelCase												=  get_size_dict(SCREAMING_SNAKE_CASE_      ,default_to_square=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  crop_size if crop_size is not None else self.crop_size
				lowerCAmelCase												=  get_size_dict(SCREAMING_SNAKE_CASE_      ,param_name='''crop_size'''			)

				lowerCAmelCase												=  make_list_of_images(SCREAMING_SNAKE_CASE_			)

				if not valid_images(SCREAMING_SNAKE_CASE_			):
					raise ValueError(
					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

				if do_resize and size is None:
					raise ValueError('''Size must be specified if do_resize is True.'''			)

				if do_rescale and rescale_factor is None:
					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

				if do_center_crop and crop_size is None:
					raise ValueError('''Crop size must be specified if do_center_crop is True.'''			)

				# All transformations expect numpy arrays.
				lowerCAmelCase												=  [to_numpy_array(SCREAMING_SNAKE_CASE_			) for image in images]

				if do_resize:
					lowerCAmelCase												=  [self.resize(image=SCREAMING_SNAKE_CASE_      ,size=SCREAMING_SNAKE_CASE_      ,resample=SCREAMING_SNAKE_CASE_			) for image in images]

				if do_center_crop:
					lowerCAmelCase												=  [self.center_crop(image=SCREAMING_SNAKE_CASE_      ,size=SCREAMING_SNAKE_CASE_			) for image in images]

				if do_rescale:
					lowerCAmelCase												=  [self.rescale(image=SCREAMING_SNAKE_CASE_      ,scale=SCREAMING_SNAKE_CASE_			) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
					lowerCAmelCase												=  [self.flip_channel_order(image=SCREAMING_SNAKE_CASE_			) for image in images]

				lowerCAmelCase												=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) for image in images]

				lowerCAmelCase												=  {'''pixel_values''': images}
				return BatchFeature(data=SCREAMING_SNAKE_CASE_      ,tensor_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	Dict      ,SCREAMING_SNAKE_CASE_  :	List[Tuple] = None			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
					if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
						raise ValueError(
						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''			)

					if is_torch_tensor(SCREAMING_SNAKE_CASE_			):
						lowerCAmelCase												=  target_sizes.numpy()

					lowerCAmelCase												=  []

					for idx in range(len(SCREAMING_SNAKE_CASE_			)			):
						lowerCAmelCase												=  torch.nn.functional.interpolate(
						    logits[idx].unsqueeze(dim=0			)      ,size=target_sizes[idx]      ,mode='''bilinear'''      ,align_corners=SCREAMING_SNAKE_CASE_			)
						lowerCAmelCase												=  resized_logits[0].argmax(dim=0			)
						semantic_segmentation.append(SCREAMING_SNAKE_CASE_			)
				else:
					lowerCAmelCase												=  logits.argmax(dim=1			)
					lowerCAmelCase												=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]			)]

				return semantic_segmentation




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


